{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Goodreads \"Classics\": Trends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By [Maria Antoniak](https://maria-antoniak.github.io/) and [Melanie Walsh](https://melaniewalsh.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains some of the Python code behind our article \"The Goodreads 'Classics': A Computational Study of Readers, Amazon, and Crowdsourced Amateur Criticism.\"\n",
    "\n",
    "In this notebook, we analyze some of the overall trends in our dataset of Goodreads reviews (figures 4-5), some of the major patterns in Goodreads' default sorting algorithm (figures 6-9), and the disparity between users discussing the \"classics\" vs the \"canon\" (figure 16)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1: Load and Process Scraped Goodreads Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import necessary Python libraries and packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict, Counter\n",
    "import random\n",
    "\n",
    "#For data analysis and manipulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "#For data visualization\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import ticker\n",
    "sns.set(style='ticks', font_scale=1.2)\n",
    "\n",
    "#Set color palette\n",
    "\n",
    "['#a1c9f4', # blue\n",
    " '#ff9f9b', # orange/pink/red\n",
    " '#b9f2f0'] # teal\n",
    "\n",
    "review_source_color_palette = sns.color_palette(['#a1c9f4', '#ff9f9b', '#b9f2f0'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Establish file paths to Goodreads reviews, book metadata, and plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We scraped and pre-processed all of our Goodreads book metadata and review data with our [Goodreads scraper](https://github.com/maria-antoniak/goodreads-scraper) Python scripts. Then we saved this processed data in the locations below. Later in the notebook, we will load and combine this data so we can analyze it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directory Where All the Data Is Located\n",
    "data_directory_path = '/Volumes/Passport-1/data/goodreads/classics-shelf'\n",
    "\n",
    "# Classic Books Metadata (Pre-processed)\n",
    "processed_books_path              = data_directory_path + '/books.processed.json'\n",
    "\n",
    "# Goodreads Reviews of Classics (Pre-processed)\n",
    "processed_reviews_newest_path     = data_directory_path + '/reviews_newest.english.processed.json'\n",
    "processed_reviews_oldest_path     = data_directory_path + '/reviews_oldest.english.processed.json'\n",
    "processed_reviews_most_liked_path = data_directory_path + '/reviews_most_liked.english.processed.json'\n",
    "\n",
    "#Directory Where Plots Will Be Output\n",
    "plots_directory_path = '/Volumes/Passport-1/output/goodreads/classics/plots'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create functions for loading and processing book/review data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Goodreads ratings to integers\n",
    "def convert_rating_to_int(rating):\n",
    "    if not pd.isnull(rating):\n",
    "        return int(rating)\n",
    "    return 'No Rating'\n",
    "\n",
    "# Get publication year for books\n",
    "def get_year(text):\n",
    "    return text.split(',')[-1].strip()\n",
    "\n",
    "# Evaluate if review contains a spoiler alert\n",
    "def get_if_spoiler(text):\n",
    "    if '(view spoiler)' in text:\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "# Evaluate if the reviewer shelved the book as a classic\n",
    "def is_shelved_as_classic(shelves):\n",
    "    if 'classic' in shelves or 'classics' in shelves:\n",
    "            return 'True'\n",
    "    return False\n",
    "\n",
    "# Calculate how many tokens there are in the review\n",
    "def get_num_tokens(tokens):\n",
    "    return len(tokens)\n",
    "\n",
    "# Load the reviews, make a dataframe, and use all previous functions to add new columns\n",
    "def load_reviews_into_df(reviews_path):\n",
    "    \n",
    "    reviews = json.load(open(reviews_path, 'r'))\n",
    "    reviews_df = pd.DataFrame(reviews)\n",
    "    \n",
    "    reviews_df['isShelvedAsClassic'] = reviews_df['reviewerShelves'].apply(is_shelved_as_classic)\n",
    "    reviews_df['rating'] = reviews_df['rating'].apply(convert_rating_to_int)\n",
    "    reviews_df['rating'] = pd.to_numeric(reviews_df['rating'], errors='ignore')\n",
    "    reviews_df['rating'] = reviews_df['rating'].apply(str)\n",
    "    reviews_df['reviewYear'] = reviews_df['date'].apply(get_year)\n",
    "    reviews_df['containsSpoilerAlert'] = reviews_df['originalText'].apply(get_if_spoiler)\n",
    "    reviews_df['numTokens'] = reviews_df['processedTokens'].apply(get_num_tokens)\n",
    "\n",
    "    return reviews_df\n",
    "\n",
    "# Evaluate if the book was published on or after 1945\n",
    "def is_post_1945(year):\n",
    "    if year >= 1945:\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "# Evaluate if book is \"most read\"\n",
    "def is_most_read(book_id):\n",
    "    if book_id in most_read_books:\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "# Evaluate if book is most popular\n",
    "def is_most_popular(book_id):\n",
    "    if book_id in most_popular_books:\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Dataframe of Classic Books Metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create lists of the most read and most popular classic books."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_read_books_file = open(data_directory_path + '/most_read_books.classics.2019_09_11.txt', 'r')\n",
    "most_popular_books_file = open(data_directory_path + '/most_popular_books_100.classics.txt', 'r')\n",
    "\n",
    "most_read_books = []\n",
    "for line in most_read_books_file:\n",
    "    if line.strip():\n",
    "        most_read_books.append(line.strip())\n",
    "        \n",
    "most_popular_books = []\n",
    "for line in most_popular_books_file:\n",
    "    if line.strip():\n",
    "        most_popular_books.append(line.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a Pandas DataFrame of our book metadata and display three random rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bookID</th>\n",
       "      <th>isbn</th>\n",
       "      <th>yearFirstPublished</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>numPages</th>\n",
       "      <th>numRatings</th>\n",
       "      <th>numReviews</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>ratingDistribution</th>\n",
       "      <th>lists</th>\n",
       "      <th>shelves</th>\n",
       "      <th>genres</th>\n",
       "      <th>yearFirstPublished_binned</th>\n",
       "      <th>isPost1945</th>\n",
       "      <th>isMostRead</th>\n",
       "      <th>isMostPopular</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>295.Treasure_Island</td>\n",
       "      <td>0753453800</td>\n",
       "      <td>1882</td>\n",
       "      <td>Treasure Island</td>\n",
       "      <td>Robert Louis Stevenson</td>\n",
       "      <td>311</td>\n",
       "      <td>362797</td>\n",
       "      <td>9998</td>\n",
       "      <td>3.83</td>\n",
       "      <td>[100818, 134127, 99226, 22110, 6516]</td>\n",
       "      <td>{'Best Books Ever': 0.0035710420950113625, 'Be...</td>\n",
       "      <td>{'to-read': 138870, 'currently-reading': 20769...</td>\n",
       "      <td>[Classics, Fiction, Adventure, Young Adult]</td>\n",
       "      <td>(1872.7, 1937.85]</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>12996.Othello</td>\n",
       "      <td>0743477553</td>\n",
       "      <td>1603</td>\n",
       "      <td>Othello</td>\n",
       "      <td>William Shakespeare</td>\n",
       "      <td>314</td>\n",
       "      <td>288265</td>\n",
       "      <td>5603</td>\n",
       "      <td>3.89</td>\n",
       "      <td>[89343, 104221, 71826, 18126, 4749]</td>\n",
       "      <td>{'Best Books Ever': 0.004851567290697255, 'Boo...</td>\n",
       "      <td>{'to-read': 89083, 'classics': 8129, 'currentl...</td>\n",
       "      <td>[Classics, Plays, Drama, Fiction, Academic &gt; S...</td>\n",
       "      <td>(1546.95, 1612.1]</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>320.One_Hundred_Years_of_Solitude</td>\n",
       "      <td>0060531045</td>\n",
       "      <td>1967</td>\n",
       "      <td>One Hundred Years of Solitude</td>\n",
       "      <td>Gabriel García Márquez</td>\n",
       "      <td>417</td>\n",
       "      <td>678054</td>\n",
       "      <td>26874</td>\n",
       "      <td>4.06</td>\n",
       "      <td>[320971, 182580, 101370, 42808, 30325]</td>\n",
       "      <td>{'Best Books Ever': 0.0006823487161070211, 'Be...</td>\n",
       "      <td>{'to-read': 7559, 'currently-reading': 295, 'f...</td>\n",
       "      <td>[Fiction, Classics, Magical Realism, Literature]</td>\n",
       "      <td>(1937.85, 2003.0]</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                bookID        isbn  yearFirstPublished  \\\n",
       "17                 295.Treasure_Island  0753453800                1882   \n",
       "96                       12996.Othello  0743477553                1603   \n",
       "130  320.One_Hundred_Years_of_Solitude  0060531045                1967   \n",
       "\n",
       "                             title                  author  numPages  \\\n",
       "17                 Treasure Island  Robert Louis Stevenson       311   \n",
       "96                         Othello     William Shakespeare       314   \n",
       "130  One Hundred Years of Solitude  Gabriel García Márquez       417   \n",
       "\n",
       "    numRatings numReviews averageRating  \\\n",
       "17      362797       9998          3.83   \n",
       "96      288265       5603          3.89   \n",
       "130     678054      26874          4.06   \n",
       "\n",
       "                         ratingDistribution  \\\n",
       "17     [100818, 134127, 99226, 22110, 6516]   \n",
       "96      [89343, 104221, 71826, 18126, 4749]   \n",
       "130  [320971, 182580, 101370, 42808, 30325]   \n",
       "\n",
       "                                                 lists  \\\n",
       "17   {'Best Books Ever': 0.0035710420950113625, 'Be...   \n",
       "96   {'Best Books Ever': 0.004851567290697255, 'Boo...   \n",
       "130  {'Best Books Ever': 0.0006823487161070211, 'Be...   \n",
       "\n",
       "                                               shelves  \\\n",
       "17   {'to-read': 138870, 'currently-reading': 20769...   \n",
       "96   {'to-read': 89083, 'classics': 8129, 'currentl...   \n",
       "130  {'to-read': 7559, 'currently-reading': 295, 'f...   \n",
       "\n",
       "                                                genres  \\\n",
       "17         [Classics, Fiction, Adventure, Young Adult]   \n",
       "96   [Classics, Plays, Drama, Fiction, Academic > S...   \n",
       "130   [Fiction, Classics, Magical Realism, Literature]   \n",
       "\n",
       "    yearFirstPublished_binned  isPost1945  isMostRead  isMostPopular  \n",
       "17          (1872.7, 1937.85]       False        True           True  \n",
       "96          (1546.95, 1612.1]       False        True           True  \n",
       "130         (1937.85, 2003.0]        True       False           True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books = json.load(open(processed_books_path, 'r'))\n",
    "\n",
    "books_df = pd.DataFrame(books)\n",
    "\n",
    "# Create publication year column\n",
    "books_df['yearFirstPublished'] = books_df['yearFirstPublished'].apply(int)\n",
    "\n",
    "# Create publication year column but bin the years\n",
    "books_df['yearFirstPublished_binned'] = pd.cut(books_df['yearFirstPublished'], 20)\n",
    "\n",
    "# Create a column for books published after 1945\n",
    "books_df['isPost1945'] = books_df['yearFirstPublished'].apply(is_post_1945)\n",
    "\n",
    "# Create a column for most read classics\n",
    "books_df['isMostRead'] = books_df['bookID'].apply(is_most_read)\n",
    "\n",
    "#Create aa column for most shelved/popular classics\n",
    "books_df['isMostPopular'] = books_df['bookID'].apply(is_most_popular)\n",
    "\n",
    "books_df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Combined Dataframe of All Goodreads Reviews + Books Metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Pandas DataFrames of all the Goodreads reviews. Then merge all the Goodreads reviews with the books metadata and display three random rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128663\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bookID</th>\n",
       "      <th>reviewID</th>\n",
       "      <th>reviewURL</th>\n",
       "      <th>user</th>\n",
       "      <th>date</th>\n",
       "      <th>numLikes</th>\n",
       "      <th>rating</th>\n",
       "      <th>originalText</th>\n",
       "      <th>processedTokens</th>\n",
       "      <th>reviewerShelves</th>\n",
       "      <th>...</th>\n",
       "      <th>numReviews</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>ratingDistribution</th>\n",
       "      <th>lists</th>\n",
       "      <th>shelves</th>\n",
       "      <th>genres</th>\n",
       "      <th>yearFirstPublished_binned</th>\n",
       "      <th>isPost1945</th>\n",
       "      <th>isMostRead</th>\n",
       "      <th>isMostPopular</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16259</th>\n",
       "      <td>11486.The_Color_Purple</td>\n",
       "      <td>review_1900777629</td>\n",
       "      <td>https://www.goodreads.com/book/show/11486.The_...</td>\n",
       "      <td>/user/show/6942006-suzy</td>\n",
       "      <td>Feb 13, 2017</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5</td>\n",
       "      <td>What a delight to reread The Color Purple afte...</td>\n",
       "      <td>[what, a, delight, to, reread, the, color, pur...</td>\n",
       "      <td>[audio-faves, historical-fiction, re-reads, fo...</td>\n",
       "      <td>...</td>\n",
       "      <td>12718</td>\n",
       "      <td>4.20</td>\n",
       "      <td>[213185, 162615, 69323, 15780, 6840]</td>\n",
       "      <td>{'Best Books Ever': 0.0014067741586408398, 'Bo...</td>\n",
       "      <td>{'to-read': 294845, 'currently-reading': 7888,...</td>\n",
       "      <td>[Classics, Fiction, Historical &gt; Historical Fi...</td>\n",
       "      <td>(1937.85, 2003.0]</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7272</th>\n",
       "      <td>157993.The_Little_Prince</td>\n",
       "      <td>review_620222</td>\n",
       "      <td>https://www.goodreads.com/book/show/157993.The...</td>\n",
       "      <td>/user/show/50687-akemi</td>\n",
       "      <td>Apr 07, 2007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>take a step back and see what's important, how...</td>\n",
       "      <td>[take, a, step, back, and, see, what, s, impor...</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>33184</td>\n",
       "      <td>4.30</td>\n",
       "      <td>[632787, 297179, 145251, 42460, 20228]</td>\n",
       "      <td>{'Books That Everyone Should Read At Least Onc...</td>\n",
       "      <td>{'to-read': 775, 'favorites': 16666, 'currentl...</td>\n",
       "      <td>[Classics, Fiction, Fantasy, Childrens]</td>\n",
       "      <td>(1937.85, 2003.0]</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33150</th>\n",
       "      <td>52357.Beowulf</td>\n",
       "      <td>review_3929241</td>\n",
       "      <td>https://www.goodreads.com/book/show/52357.Beowulf</td>\n",
       "      <td>/user/show/169355-kate</td>\n",
       "      <td>Aug 01, 2007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>I read this aloud to my husband on a road trip...</td>\n",
       "      <td>[i, read, this, aloud, to, my, husband, on, a,...</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>5864</td>\n",
       "      <td>3.43</td>\n",
       "      <td>[43893, 59416, 65131, 28969, 13632]</td>\n",
       "      <td>{'Best Books Ever': 0.006582981639793673, 'Boo...</td>\n",
       "      <td>{'to-read': 71097, 'classics': 7087, 'poetry':...</td>\n",
       "      <td>[Classics, Poetry, Fiction, Fantasy, Academic ...</td>\n",
       "      <td>(960.6, 1025.75]</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         bookID           reviewID  \\\n",
       "16259    11486.The_Color_Purple  review_1900777629   \n",
       "7272   157993.The_Little_Prince      review_620222   \n",
       "33150             52357.Beowulf     review_3929241   \n",
       "\n",
       "                                               reviewURL  \\\n",
       "16259  https://www.goodreads.com/book/show/11486.The_...   \n",
       "7272   https://www.goodreads.com/book/show/157993.The...   \n",
       "33150  https://www.goodreads.com/book/show/52357.Beowulf   \n",
       "\n",
       "                          user          date  numLikes rating  \\\n",
       "16259  /user/show/6942006-suzy  Feb 13, 2017      17.0      5   \n",
       "7272    /user/show/50687-akemi  Apr 07, 2007       0.0      5   \n",
       "33150   /user/show/169355-kate  Aug 01, 2007       0.0      4   \n",
       "\n",
       "                                            originalText  \\\n",
       "16259  What a delight to reread The Color Purple afte...   \n",
       "7272   take a step back and see what's important, how...   \n",
       "33150  I read this aloud to my husband on a road trip...   \n",
       "\n",
       "                                         processedTokens  \\\n",
       "16259  [what, a, delight, to, reread, the, color, pur...   \n",
       "7272   [take, a, step, back, and, see, what, s, impor...   \n",
       "33150  [i, read, this, aloud, to, my, husband, on, a,...   \n",
       "\n",
       "                                         reviewerShelves  ... numReviews  \\\n",
       "16259  [audio-faves, historical-fiction, re-reads, fo...  ...      12718   \n",
       "7272                                                  []  ...      33184   \n",
       "33150                                                 []  ...       5864   \n",
       "\n",
       "      averageRating                      ratingDistribution  \\\n",
       "16259          4.20    [213185, 162615, 69323, 15780, 6840]   \n",
       "7272           4.30  [632787, 297179, 145251, 42460, 20228]   \n",
       "33150          3.43     [43893, 59416, 65131, 28969, 13632]   \n",
       "\n",
       "                                                   lists  \\\n",
       "16259  {'Best Books Ever': 0.0014067741586408398, 'Bo...   \n",
       "7272   {'Books That Everyone Should Read At Least Onc...   \n",
       "33150  {'Best Books Ever': 0.006582981639793673, 'Boo...   \n",
       "\n",
       "                                                 shelves  \\\n",
       "16259  {'to-read': 294845, 'currently-reading': 7888,...   \n",
       "7272   {'to-read': 775, 'favorites': 16666, 'currentl...   \n",
       "33150  {'to-read': 71097, 'classics': 7087, 'poetry':...   \n",
       "\n",
       "                                                  genres  \\\n",
       "16259  [Classics, Fiction, Historical > Historical Fi...   \n",
       "7272             [Classics, Fiction, Fantasy, Childrens]   \n",
       "33150  [Classics, Poetry, Fiction, Fantasy, Academic ...   \n",
       "\n",
       "       yearFirstPublished_binned isPost1945 isMostRead  isMostPopular  \n",
       "16259          (1937.85, 2003.0]       True       True           True  \n",
       "7272           (1937.85, 2003.0]      False       True           True  \n",
       "33150           (960.6, 1025.75]      False       True           True  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews_newest_df = load_reviews_into_df(processed_reviews_newest_path)\n",
    "reviews_oldest_df = load_reviews_into_df(processed_reviews_oldest_path)\n",
    "reviews_most_liked_df = load_reviews_into_df(processed_reviews_most_liked_path)\n",
    "\n",
    "reviews_newest_df['reviewSource'] = 'Newest'\n",
    "reviews_oldest_df['reviewSource'] = 'Oldest'\n",
    "reviews_most_liked_df['reviewSource'] = 'Default'\n",
    "\n",
    "combined_df_newest = pd.merge(reviews_newest_df, books_df, on='bookID', how='outer')\n",
    "combined_df_oldest = pd.merge(reviews_oldest_df, books_df, on='bookID', how='outer')\n",
    "combined_df_most_liked = pd.merge(reviews_most_liked_df, books_df, on='bookID', how='outer')\n",
    "\n",
    "combined_df = combined_df_newest.append(combined_df_oldest).append(combined_df_most_liked)\n",
    "\n",
    "print(len(combined_df.index))\n",
    "\n",
    "combined_df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Explore Overall Trends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Percentages to Bar Chart Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we make a function that will add percentages to our bar charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_percents_to_bars(df, ax, orientation, y_offset=0.5, x_offset=1, total=None):\n",
    "    if not total:\n",
    "        total = len(df.index)\n",
    "    if orientation == 'h':\n",
    "        percents = ['{:.1f}%'.format(100*p.get_width()/total) for p in ax.patches]\n",
    "        bar_widths = [p.get_width() for p in ax.patches]\n",
    "        bar_heights = [p.get_xy()[1] for p in ax.patches]\n",
    "        for i, _percent in enumerate(percents):\n",
    "            plt.text(x=bar_widths[i]+x_offset, y=bar_heights[i]+y_offset, s=_percent, size=11)\n",
    "    elif orientation == 'v':\n",
    "        percents = ['{:.1f}%'.format(100*p.get_height()/total) for p in ax.patches]\n",
    "        bar_widths = [p.get_x() for p in ax.patches]\n",
    "        bar_heights = [p.get_height() for p in ax.patches]\n",
    "        for i, _percent in enumerate(percents):\n",
    "            plt.text(x=bar_widths[i]+x_offset, y=bar_heights[i]+y_offset, s=_percent, size=11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timeline of Goodreads Reviews (Figure 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we plot number of Goodreads reviews in our dataset published per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "sns.set(style='ticks', font_scale=1.4)\n",
    "plt.figure(figsize=(10,3))\n",
    "ax = sns.countplot(data=combined_df, \n",
    "                   x='reviewYear',\n",
    "                   color='skyblue',\n",
    "                   order=['2007', '2008', '2009', '2010', '2011', '2012', \n",
    "                          '2013', '2014', '2015', '2016', '2017', '2018', '2019'])\n",
    "\n",
    "add_percents_to_bars(combined_df, ax, orientation='v', y_offset=1000, x_offset=0.1, total=None)\n",
    "\n",
    "plt.ylabel('# of Reviews')\n",
    "plt.xlabel('Year')\n",
    "ax.yaxis.set_major_formatter(ticker.EngFormatter())\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "# plt.savefig(plots_directory_path + '/barplot_date_distribution.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rating Distribution of Goodreads Reviews (Figure 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we plot the rating distribution of Goodreads reviews in our dataset published per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,3))\n",
    "ax = sns.countplot(data=combined_df, \n",
    "                   x='rating',\n",
    "                   color='skyblue',\n",
    "                   order=['1', '2', '3', '4', '5'])\n",
    "add_percents_to_bars(combined_df, ax, orientation='v', y_offset=1000, x_offset=0.1, total=None)\n",
    "plt.ylabel('Number of Reviews')\n",
    "plt.xlabel('Rating')\n",
    "ax.yaxis.set_major_formatter(ticker.EngFormatter())\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "# plt.savefig(plots_directory_path + '/barplot_rating_distribution_without_unrated.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Corpus Statistics for Goodreads Reviews (Table 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we calculate the number of reviews, the mean length of reviews, and the number of Goodreads users in each sorting category. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF REVIEWS\n",
      "Newest 43167\n",
      "Oldest 42611\n",
      "Default 42884\n",
      "128663\n",
      "\n",
      "LENGTH OF REVIEWS\n",
      "Newest 91.00331271573192\n",
      "Oldest 54.603506136913005\n",
      "Default 261.2218076671952\n",
      "135.68225519380087\n",
      "\n",
      "USERS\n",
      "Newest 34220 1.2614552893045003\n",
      "Oldest 24292 1.7541165815906472\n",
      "Default 17362 2.4699919364128555\n",
      "Total 69574 1.8492971512346565\n"
     ]
    }
   ],
   "source": [
    "source_count_dict = defaultdict(int)\n",
    "source_lengths_dict = defaultdict(list)\n",
    "source_user_count_dict = defaultdict(lambda: defaultdict(int))\n",
    "user_count_dict = defaultdict(int)\n",
    "\n",
    "for _index, _row in combined_df.iterrows():\n",
    "    \n",
    "    source_count_dict[_row['reviewSource']] += 1\n",
    "    \n",
    "    source_user_count_dict[_row['reviewSource']][_row['user']] += 1\n",
    "    user_count_dict[_row['user']] += 1\n",
    "    \n",
    "    if not pd.isnull(_row['originalText']):\n",
    "        source_lengths_dict[_row['reviewSource']].append(len(_row['processedTokens']))\n",
    "    else:\n",
    "        source_lengths_dict[_row['reviewSource']].append(0)\n",
    "    \n",
    "print('NUMBER OF REVIEWS')\n",
    "for _source in ['Newest', 'Oldest', 'Default']:\n",
    "    print(_source, source_count_dict[_source])\n",
    "print(sum(source_count_dict.values()))\n",
    "\n",
    "print()\n",
    "\n",
    "print('LENGTH OF REVIEWS')\n",
    "for _source in ['Newest', 'Oldest', 'Default']:\n",
    "    print(_source, np.mean(source_lengths_dict[_source]))\n",
    "print(np.mean([l for _source, _lengths in source_lengths_dict.items() for l in _lengths]))\n",
    "\n",
    "print()\n",
    "\n",
    "print('USERS')\n",
    "for _source in ['Newest', 'Oldest', 'Default']:\n",
    "    _user_count_dict = source_user_count_dict[_source]\n",
    "    print(_source, len(_user_count_dict), np.mean(list(_user_count_dict.values())))\n",
    "print('Total', len(user_count_dict), np.mean(list(user_count_dict.values())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3: Analyze Goodreads Data and Make Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Number of Likes Per Goodreads Review (Figure 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we plot the mean number of likes for Goodreads reviews in each sort setting.\n",
    "\n",
    "We also use bootstrapped sampling to estimate the amount of instability in the results when a certain book is included or removed. We calculate the mean number of likes for each of 20 bootstrapped samples, or 20 random samples with replacement, and plot the standard deviation across these samples as an error bar in the bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "<Figure size 288x216 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bootstrapped_dicts = []\n",
    "\n",
    "for i in range(0, 20):\n",
    "\n",
    "    _sampled_books = random.choices([b['bookID'] for b in books], k=len(books))\n",
    "\n",
    "    _order_likes_dict = defaultdict(list)\n",
    "    for _book_id in _sampled_books:\n",
    "        for i, r in combined_df[combined_df['bookID'] == _book_id].iterrows(): \n",
    "            _order_likes_dict[r['reviewSource']].append(r['numLikes'])\n",
    "\n",
    "    for _order, _likes in _order_likes_dict.items():\n",
    "        bootstrapped_dicts.append({'reviewSource': _order,\n",
    "                                   'numLikes': np.mean(_likes)})\n",
    "   \n",
    "bootstrapped_df = pd.DataFrame(bootstrapped_dicts)\n",
    "\n",
    "plt.figure(figsize=(4, 3))\n",
    "sns.barplot(data=bootstrapped_df, \n",
    "            x='reviewSource',\n",
    "            y='numLikes',\n",
    "            palette=review_source_color_palette,\n",
    "            errwidth=1, \n",
    "            capsize=0.2,\n",
    "            ci='sd')\n",
    "plt.xlabel('Reviews Sort Order')\n",
    "plt.ylabel('Mean # of Likes per Review')\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "# plt.savefig(plots_directory_path + '/barplot_source_x_likes.bootsrapped.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Number of Words Per Review (Figure 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we plot the mean number of words for Goodreads reviews in each sort setting.\n",
    "\n",
    "We also use bootstrapped sampling to estimate the amount of instability in the results when a certain book is included or removed. We calculate the mean number of words for each of 20 bootstrapped samples, or 20 random samples with replacement, and plot the standard deviation across these samples as an error bar in the bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "bootstrapped_dicts = []\n",
    "\n",
    "for i in range(0, 20):\n",
    "\n",
    "    _sampled_books = random.choices([b['bookID'] for b in books], k=len(books))\n",
    "\n",
    "    _order_length_dict = defaultdict(list)\n",
    "    for _book_id in _sampled_books:\n",
    "        for i, r in combined_df[combined_df['bookID'] == _book_id].iterrows(): \n",
    "            _order_length_dict[r['reviewSource']].append(r['numTokens'])\n",
    "\n",
    "    for _order, _lengths in _order_length_dict.items():\n",
    "        bootstrapped_dicts.append({'reviewSource': _order,\n",
    "                                   'numTokens': np.mean(_lengths)})\n",
    "   \n",
    "bootstrapped_df = pd.DataFrame(bootstrapped_dicts)\n",
    "\n",
    "plt.figure(figsize=(4, 3))\n",
    "sns.barplot(data=bootstrapped_df, \n",
    "            x='reviewSource',\n",
    "            y='numTokens',\n",
    "            palette=review_source_color_palette,\n",
    "            errwidth=1, \n",
    "            capsize=0.2,\n",
    "            ci='sd')\n",
    "plt.xlabel('Reviews Sort Order')\n",
    "plt.ylabel('Mean # of Words per Review')\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "# plt.savefig(plots_directory_path + '/barplot_source_x_length.bootsrapped.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of \"Updated\" Goodreads Reviews (Figure 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we calculate how many reviews mention \"update\"/\"updated\" across default, oldest, and newest reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We loop through the Pandas dataframe and check to see if any of the seed tokens (\"update\"/\"updated\") appear in the column \"processedTokens\" for each Goodreads review. Then we create a new transformed dataframe `df_to_plot` for plotting purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Newest 68\n",
      "Oldest 102\n",
      "Default 429\n"
     ]
    }
   ],
   "source": [
    "source_update_count_dict = defaultdict(int)\n",
    "\n",
    "dicts_to_plot = []\n",
    "\n",
    "for _index, _row in combined_df.iterrows():\n",
    "    \n",
    "    if not pd.isnull(_row['originalText']) and len(_row['processedTokens']) > 0:\n",
    "                    \n",
    "        if any(s in _row['processedTokens'] for s in ['update', 'updated']):\n",
    "            source_update_count_dict[_row['reviewSource']] += 1\n",
    "            dicts_to_plot.append({'Review Source': _row['reviewSource']})\n",
    "                \n",
    "for _source, _count in source_update_count_dict.items():\n",
    "    print(_source, _count)\n",
    "    \n",
    "df_to_plot = pd.DataFrame(dicts_to_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot our new dataframe `df_to_plot` with Seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     "metadata": {},
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   ],
   "source": [
    "plt.figure(figsize=(4,3))\n",
    "ax = sns.countplot(data=df_to_plot, \n",
    "                   x='Review Source',\n",
    "                   palette=review_source_color_palette)\n",
    "add_percents_to_bars(df_to_plot, ax, orientation='v', y_offset=10, x_offset=0.3, total=len(combined_df.index))\n",
    "sns.despine()\n",
    "plt.ylabel('# of Reviews Containing Updates')\n",
    "plt.xlabel('Reviews Sort Order')\n",
    "plt.tight_layout()\n",
    "# plt.savefig(plots_directory_path + '/barplot_updated_updated.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of Reviews with Spoiler Alerts (Figure 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we calculate how many reviews include a spoiler alert across default, oldest, and newest reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot only the Goodreads reviews from our main dataframe that contained a spoiler alert (aka rows that contained the value `True` in the column \"containsSpoilerAlert\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
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       "<Figure size 288x216 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3))\n",
    "ax = sns.countplot(data=combined_df[combined_df.containsSpoilerAlert == True], \n",
    "                   x='reviewSource',\n",
    "                   palette=review_source_color_palette)\n",
    "plt.ylabel('# of Reviews with Spoiler Alert')\n",
    "plt.xlabel('Reviews Sort Order')\n",
    "add_percents_to_bars(combined_df, ax, orientation='v', y_offset=10, x_offset=0.3, total=len(combined_df.index))\n",
    "sns.despine()\n",
    "# plt.savefig(plots_directory_path + '/barplot_spoiler_alerts.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Classic vs. Canon (Figure 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we calculate how many Goodreads reviews mention \"classic\"/\"classics\" vs. \"canon\"/\"canonical.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We loop through the Pandas dataframe and check to see if any of the seed tokens (\"classic\"/\"classics\"/\"canon\"/\"canonical\") appear in the column \"processedTokens\" for each Goodreads review. Then we create a new transformed dataframe `df_to_plot` for plotting purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "257096"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classics_seeds = [['classic', 'classics'],\n",
    "                  ['canon', 'canonical']]\n",
    "\n",
    "def get_polarity(_rating):\n",
    "    if _rating in ['1', '2', '3']:\n",
    "        return 'Negative'\n",
    "    if _rating in ['4', '5']:\n",
    "        return 'Positive'\n",
    "    return 'Unknown'\n",
    "\n",
    "\n",
    "dicts_to_plot = []\n",
    "\n",
    "for _index, _row in combined_df.iterrows():\n",
    "    \n",
    "    if not pd.isnull(_row['originalText']) and len(_row['processedTokens']) > 0:\n",
    "        \n",
    "        for _seed_tokens in classics_seeds:\n",
    "            \n",
    "            _contains_seeds = False\n",
    "            if any(s in _row['processedTokens'] for s in _seed_tokens):\n",
    "                _contains_seeds = True\n",
    "            \n",
    "            dicts_to_plot.append({'Seeds': ' '.join(_seed_tokens),\n",
    "                                  'Contains Seeds': _contains_seeds,\n",
    "                                  'Post 1945': _row['isPost1945'],\n",
    "                                  'Polarity': get_polarity(_row['rating']),\n",
    "                                  'Most Popular': _row['isMostPopular'],\n",
    "                                  'Review Source': _row['reviewSource']})\n",
    "        \n",
    "df_to_plot = pd.DataFrame(dicts_to_plot)\n",
    "\n",
    "len(df_to_plot.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot our new dataframe `df_to_plot` with Seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
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   ],
   "source": [
    "plt.figure(figsize=(6,1.5))\n",
    "ax = sns.countplot(data=df_to_plot[df_to_plot['Contains Seeds'] == True], \n",
    "                   y='Seeds',\n",
    "                   palette=review_source_color_palette,\n",
    "                   order=['classic classics', 'canon canonical'])\n",
    "sns.despine()\n",
    "plt.xlabel('# of Reviews Containing Word(s)')\n",
    "plt.ylabel('Words')\n",
    "# lgd = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "\n",
    "add_percents_to_bars(df_to_plot, ax, orientation='h', y_offset=0.6, x_offset=100, total=len(combined_df.index))\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(plots_directory_path + '/barplot_classic_canonical_x_counts.simple.pdf')"
   ]
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